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          <h1 class="post-title" itemprop="name headline">ETF定投数据分析3——金融数据分析</h1>
        

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        <p> 上一篇文章用Python对定投数据进行了处理，计算出了总的收益率随时间的变化数据，保存到了csv文件里。现在我们就开始对数据进行具体的分析。具体如何分析，我也没谱，是第一次，走到哪儿就算哪儿吧。首先，先建立一个git分支，在分支上编辑新代码，完成以后再合并。git分支功能我以前也没怎么用过，只是知道有这么个功能。搜了一下，又动手实验，成功了。先建立名为data_analysis的分支，然后转移到该分支中:</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">git branch data_analysisgit checkout data_analysis</span><br></pre></td></tr></table></figure>
<p>或者可以直接新建分支并转移</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">git checkout -b data_analysis</span><br></pre></td></tr></table></figure>
<p>然后就可以增加代码啦。新建一个data_analysis.py的文件，用于数据分析。先从csv文件中导入数据到DataFrame变量中，再输出看看。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd    </span><br><span class="line">etf_total = pd.read_csv(<span class="string">&quot;total_etf.csv&quot;</span>)    </span><br><span class="line">etf_300 = pd.read_csv(<span class="string">&quot;300etf.csv&quot;</span>)    </span><br><span class="line">etf_nas = pd.read_csv(<span class="string">&quot;nasetf.csv&quot;</span>)    </span><br><span class="line">print(etf_total.head())    </span><br><span class="line">print(etf_300.head())    </span><br><span class="line">print(etf_nas.head())</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/01.png"></p>
<p>没问题啦。再提交代码。最后将本地分支推送到github上。</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">git push -u origin data_analysis</span><br></pre></td></tr></table></figure>
<p>再把数据可视化一下吧，先尝试一下各种不同的图形类型。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment">#数据可视化</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">Display</span>(<span class="params">data</span>):</span>    </span><br><span class="line">    fig = plt.figure()    </span><br><span class="line">    ax1 = fig.add_subplot(<span class="number">2</span>,<span class="number">2</span>,<span class="number">1</span>)    </span><br><span class="line">    ax2 = fig.add_subplot(<span class="number">2</span>,<span class="number">2</span>,<span class="number">2</span>)    </span><br><span class="line">    ax3 = fig.add_subplot(<span class="number">2</span>,<span class="number">2</span>,<span class="number">3</span>)    </span><br><span class="line">    ax4 = fig.add_subplot(<span class="number">2</span>,<span class="number">2</span>,<span class="number">4</span>)    </span><br><span class="line">    <span class="comment">#用各种不同的形式画图    </span></span><br><span class="line">    ax1.plot(data.收益率)    </span><br><span class="line">    ax2.hist(data.收益率, bins =<span class="number">10</span>, alpha = <span class="number">0.8</span>, facecolor = <span class="string">&#x27;b&#x27;</span>, normed = <span class="number">1</span>)    </span><br><span class="line">    ax3.scatter(<span class="built_in">range</span>(<span class="built_in">len</span>(data)), data.收益率, marker =<span class="string">&#x27;.&#x27;</span>)    </span><br><span class="line">    ax4.plot(data.收益率, <span class="string">&#x27;k--&#x27;</span>)        </span><br><span class="line">    fig.savefig(<span class="string">&quot;可视化数据.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/02.png"></p>
<p>还可以用另一种方法建立子图绘图</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#另一种方法    </span></span><br><span class="line">fig,ax = plt.subplots(<span class="number">2</span>,<span class="number">2</span>)    </span><br><span class="line">ax[<span class="number">0</span>, <span class="number">0</span>].plot(data.收益率)    </span><br><span class="line">ax[<span class="number">0</span>, <span class="number">1</span>].plot(data.收益率, <span class="string">&#x27;k--&#x27;</span>)    </span><br><span class="line">ax[<span class="number">1</span>, <span class="number">0</span>].plot(data.收益率, <span class="string">&#x27;k-&#x27;</span>, drawstyle=<span class="string">&#x27;steps-post&#x27;</span>)    </span><br><span class="line">ax[<span class="number">1</span>, <span class="number">1</span>].plot(data.收益率, linestyle=<span class="string">&#x27;dashed&#x27;</span>, marker=<span class="string">&#x27;o&#x27;</span>)    fig.savefig(<span class="string">&quot;可视化数据2.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/03.png"><br>给图形增加图例</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#绘图并增加图例    </span></span><br><span class="line">fig,ax = plt.subplots(<span class="number">1</span>,<span class="number">1</span>)    </span><br><span class="line">ax.plot(data.收益率, label =<span class="string">&quot;收益率&quot;</span>)    </span><br><span class="line">fc = data.手续费/data.成本    </span><br><span class="line">ax.plot(fc, label=<span class="string">&quot;手续费占比&quot;</span>)    plt.legend(loc=<span class="string">&quot;best&quot;</span>)    </span><br><span class="line">fig.savefig(<span class="string">&quot;可视化数据3.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/04.png"><br>中文是乱码，搜了一圈，要显示中文好麻烦，貌似还要root手机，还是放弃了，就用英文吧。<br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/05.png"><br>matplotlib是一个低阶的工具，要考虑作图的很多细节。pandas还有很多高阶的绘图工具。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#用pandas画图    </span></span><br><span class="line">fig = plt.figure()    </span><br><span class="line">data.收益率.plot(kind = <span class="string">&#x27;bar&#x27;</span>)    </span><br><span class="line">fig.savefig(<span class="string">&quot;pandas作图.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/06.png"><br>横坐标有问题</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">fig = plt.figure()    </span><br><span class="line">data.收益率.hist(bins =<span class="number">20</span>, normed = <span class="literal">True</span>)    </span><br><span class="line">fig.savefig(<span class="string">&quot;pandas作图2.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/07.png"><br>可视化显示就到这里吧，下面再试试用时间序列分析。pandas提供了很多时间序列分析工具。移动窗口分析，一看就是均线嘛。想当初我分析定投的时候还自己从数据里算均线，结果人家有现成的！画月线和双月线。书上的方法是用rolling_mean，结果提示该函数会被废弃，于是照其提示用最新的。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#移动时间窗口分析</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">MovementWindows</span>(<span class="params">data</span>):</span>    </span><br><span class="line">    mean_30 = data.收益率.rolling(window=<span class="number">30</span>, center = <span class="literal">False</span>).mean()          mean_60 = data.收益率.rolling(window=<span class="number">60</span>, center = <span class="literal">False</span>).mean()          fig = plt.figure()    </span><br><span class="line">    data.收益率.plot()    </span><br><span class="line">    mean_30.plot()    </span><br><span class="line">    mean_60.plot()    </span><br><span class="line">    fig.savefig(<span class="string">&quot;均线图&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/08.png"><br>同理可以算出标准差的移动窗口</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">std_30 = data.收益率.rolling(window=<span class="number">30</span>, center = <span class="literal">False</span>).std()    </span><br><span class="line">std_60 = data.收益率.rolling(window=<span class="number">60</span>, center = <span class="literal">False</span>).std()    </span><br><span class="line">fig = plt.figure()    </span><br><span class="line">std_30.plot(label=<span class="string">&quot;30std&quot;</span>)    std_60.plot(label=<span class="string">&quot;60std&quot;</span>)    plt.legend(loc=<span class="string">&quot;best&quot;</span>)    </span><br><span class="line">fig.savefig(<span class="string">&quot;标准差图.png&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/blog0087-etfinverstment/09.png"><br>这次先到这里吧，还是参考的《Python for data analysis》。我发文章的两个地方，欢迎大家在朋友圈等地方分享，欢迎点“好看”。谢谢。我的个人博客地址：<a href="https://zwdnet.github.io我的微信个人订阅号：赵瑜敏的口腔医学学习园地">https://zwdnet.github.io我的微信个人订阅号：赵瑜敏的口腔医学学习园地</a><br><img src="https://zymblog-1258069789.cos.ap-chengdu.myqcloud.com/other/wx.jpg"></p>

      
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